Driven by global climate change and the ongoing energy transition, the coupling between power supply capabilities and meteorological factors has become increasingly significant. Over the long term, accurately quantifying the power generation of renewable energy under the influence of climate change is essential for the development of sustainable power systems. However, due to interdisciplinary differences in data requirements, climate data often lacks the necessary hourly resolution to capture the short-term variability and uncertainties of renewable energy resources. To address this limitation, a super-resolution recurrent diffusion model (SRDM) has been developed to enhance the temporal resolution of climate data and model the short-term uncertainty. The SRDM incorporates a pre-trained decoder and a denoising network, that generates long-term, high-resolution climate data through a recurrent coupling mechanism. The high-resolution climate data is then converted into power value using the mechanism model, enabling the simulation of wind and photovoltaic (PV) power generation on future long-term scales. Case studies were conducted in the Ejina region of Inner Mongolia, China, using fifth-generation reanalysis (ERA5) and coupled model intercomparison project (CMIP6) data under two climate pathways: SSP126 and SSP585. The results demonstrate that the SRDM outperforms existing generative models in generating super-resolution climate data. Furthermore, the research highlights the estimation biases introduced when low-resolution climate data is used for power conversion.

翻译：受全球气候变化和持续能源转型的驱动，电力供应能力与气象因素之间的耦合日益显著。长期来看，准确量化气候变化影响下可再生能源的发电量对可持续电力系统的发展至关重要。然而，由于跨学科数据需求的差异，气候数据通常缺乏捕捉可再生能源短期波动性和不确定性所需的小时级分辨率。为克服这一局限，本研究开发了一种超分辨率循环扩散模型（SRDM），用于提升气候数据的时间分辨率并建模短期不确定性。SRDM集成了预训练解码器和去噪网络，通过循环耦合机制生成长期、高分辨率的气候数据。随后，利用机理模型将高分辨率气候数据转换为功率值，从而实现对未来长期尺度上风电和光伏发电的模拟。以中国内蒙古额济纳地区为例，基于第五代再分析数据（ERA5）和耦合模式比较计划（CMIP6）数据，在SSP126和SSP585两种气候路径下开展案例研究。结果表明，SRDM在生成超分辨率气候数据方面优于现有生成模型。此外，研究揭示了低分辨率气候数据用于功率转换时引入的估计偏差。